Indonesian RoBERTa Base Sentiment Classifier is a sentiment-text-classification model based on the RoBERTa model. The model was originally the pre-trained Indonesian RoBERTa Base model, which is then fine-tuned on
SmSA dataset consisting of Indonesian comments and reviews.
After training, the model achieved an evaluation accuracy of 94.36% and F1-macro of 92.42%. On the benchmark test set, the model achieved an accuracy of 93.2% and F1-macro of 91.02%.
Trainer class from the Transformers library was used to train the model. PyTorch was used as the backend framework during training, but the model remains compatible with other frameworks nonetheless.
|Model||#params||Arch.||Training/Validation data (text)|
The model was trained for 5 epochs and the best model was loaded at the end.
|Epoch||Training Loss||Validation Loss||Accuracy||F1||Precision||Recall|
from transformers import pipeline pretrained_name = "w11wo/indonesian-roberta-base-sentiment-classifier" nlp = pipeline( "sentiment-analysis", model=pretrained_name, tokenizer=pretrained_name ) nlp("Jangan sampai saya telpon bos saya ya!")
Do consider the biases which come from both the pre-trained RoBERTa model and the
SmSA dataset that may be carried over into the results of this model.
Indonesian RoBERTa Base Sentiment Classifier was trained and evaluated by Wilson Wongso. All computation and development are done on Google Colaboratory using their free GPU access.
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